Optical Machine Learning: Harnessing Multiple Scattering for Efficiency

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Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Neural Signals and Signal Processing

Delves into neural signals, GLM, ANOVA, brain mapping, connectivity, and multivariate methods.

Photonic Extreme Learning Machines: Reservoir Computing Techniques

Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.

Phase Transitions in Physics and Machine Learning

Explores phase transitions in physics and computational problems, highlighting challenges faced by algorithms and the application of physics principles in understanding neural networks.

Neural Signals and Signal Processing

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Neural Signals and Signal Processing

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Machine Learning for Feature Extraction

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Neural Networks Recap: Activation Functions

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